Chapter: Artificial Intelligence

Learning - Artificial Intelligence

Learning process is the basis of knowledge acquisition process. Knowledge acquisition is the expanding the capabilities of a system or improving its performance at some specified task.

LEARNING

Learning process is the basis of knowledge acquisition process.
Knowledge acquisition is the expanding the capabilities of a system or
improving its performance at some specified task. So we can say knowledge
acquisition is the goal oriented creation and refinement of knowledge. The
acquired knowledge may consist of various facts, rules, concepts, procedures,
heuristics, formulas, relationships or any other useful information. Knowledge
can be acquired from various sources like, domain of interests, text books,
technical papers, databases, reports. The terms of increasing levels of
abstraction, knowledge includes data, information and Meta knowledge. Meta
knowledge includes the ability to evaluate the knowledge available, the
additional knowledge required and the systematic implied by the present rules.

Learning involves generalization from experience. Computer system is
said to have learning if it is able to not only do the “repetition of same
task” more effe ctively, but also the similar tasks of the related domain. Learning
is possible due to some factors like the skill refinement and knowledge
acquisition. Skill refinement refers to the situation of improving the skill by
performing the same task again and again. If machines are able to improve their
skills with the handling of task, they can be said having skill of learning. On
the other hand, as we are able to remember the experience or gain some
knowledge by handling the task, so we can improve our skill. We would like our
learning algorithms to be efficient in three respects:

(1)Computational: Number of computations during training and during recognition.

(2)Statistical: Number of examples required for good generalization, especially labeled
data.

Human
Involvement: Specify the prior knowledge built
into the model before training.A
similar machine learning architecture is given in figure .

Design of learning element is dictated by the followings.

(1)What type of performance element is used?

(2)Which functional component is to be learned?

(3)How that functional component is represented?

(4)What kind of feedback is available?

(5)How can be compared between the existing feedbacks with the new data?

(6)What are the levels of comparisons? Etc.

Any system designed to create new knowledge and thereby improve its
performance must include a set of data structures that represents the system’s
present level of expertise and a task algorithm that uses the rules to guide
the system’s problem solving activity. The architecture of a general learning
procedure is given in figure .

(NIL )

Figure Architecture of Machine Learning

Hence the inputs may be any types of inputs, those are executed for
solution of a problem. Those inputs are processed to get the corresponding
results. The learning element learns some sort of knowledges by the knowledge
acquisition techniques. The acquired knowledge may be required for a same
problem in future, for which that problem can be easily solved.

Every learning model must contain implicit or explicit restrictions on
the class of functions that can learn. Among the set of all possible functions,
we are particularly interested in a subset that contains all the tasks involved
in intelligent behaviour. Examples of such tasks include visual perception,
auditory perception, planning, control etc. The set does not just include
specific visual perception tasks, but the set of all the tasks that an
intelligent agent should be able to learn. Although we may like to think that
the human brain is some what general purpose, it is extremely restricted in its
ability to learn high dimensional functions.